AI-POWERED CYBERSECURITY FOR 7G-ENABLED VIRTUAL THERAPY PLATFORMS: A MACHINE LEARNING FRAMEWORK FOR THREAT DETECTION AND PRIVACY PRESERVATION

Abstract

The emergence of 7G networks is transforming virtual therapy platforms by enabling ultra-low-latency, high-bandwidth, and immersive healthcare experiences through augmented reality (AR), virtual reality (VR), and haptic feedback technologies. However, this convergence of hyper-connectivity and sensitive healthcare data substantially expands the cyber-attack surface, introducing unprecedented threats including data breaches, adversarial AI attacks, deepfake impersonation, and privacy violations. This paper proposes a comprehensive AI-driven cybersecurity framework specifically designed for 7G-enabled virtual therapy platforms. The framework integrates five interconnected layers: Predictive Threat Detection System (PTDS) employing LSTM and clustering algorithms, Adaptive Threat Intelligence System (ATIS) incorporating Generative Adversarial Networks (GANs), Privacy-Preserving Data Management (PPDM) utilizing federated learning and homomorphic encryption, Continuous Authentication System (CAS) leveraging biometric and behavioral profiling, and a Self-Healing Cybersecurity System (SHCS) powered by reinforcement learning. Experimental evaluation in a simulated 7G environment demonstrates a threat detection accuracy of 98.5%, a 35% reduction in response time compared to conventional systems, and robust privacy preservation compliant with GDPR and HIPAA regulations. The framework establishes a foundation for secure, resilient, and trustworthy next-generation digital mental healthcare delivery.